Nvidia corporation (20240193445). DOMAIN-CUSTOMIZABLE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 DOMAIN-CUSTOMIZABLE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DOMAIN-CUSTOMIZABLE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Domain-Specific Machine Learning Models
- 1.13 Original Abstract Submitted
DOMAIN-CUSTOMIZABLE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
DOMAIN-CUSTOMIZABLE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193445 titled 'DOMAIN-CUSTOMIZABLE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
Simplified Explanation
The patent application describes systems and methods for training machine learning models, like large language models, for specific domains by dividing the model into separate parts that can be trained independently.
- The machine learning model is trained for specific domains by separating it into base models and additional parts associated with those domains.
- Each part of the model is trained separately using domain-specific data, allowing for more targeted training.
- The trained parts can be activated or deactivated based on the input data being processed, improving the model's performance for different domains.
Key Features and Innovation
- Training machine learning models for specific domains by dividing them into separate parts.
- Independent training of each part using domain-specific data.
- Dynamic activation and deactivation of parts based on input data.
Potential Applications
The technology can be applied in various fields such as natural language processing, sentiment analysis, and personalized recommendations.
Problems Solved
- Enhances the performance of machine learning models for specific domains.
- Allows for more targeted training using domain-specific data.
- Improves adaptability and efficiency of machine learning models.
Benefits
- Increased accuracy and effectiveness in processing domain-specific data.
- Enhanced performance and adaptability of machine learning models.
- Improved efficiency and customization for different domains.
Commercial Applications
Title: Domain-Specific Machine Learning Models for Enhanced Performance This technology can be utilized in industries such as e-commerce, healthcare, and finance for better data analysis, customer insights, and personalized services.
Prior Art
Further research can be conducted in the field of domain-specific machine learning models and their applications in various industries.
Frequently Updated Research
Stay updated on advancements in domain-specific machine learning models and their impact on different domains.
Questions about Domain-Specific Machine Learning Models
What are the key advantages of training machine learning models for specific domains?
Training machine learning models for specific domains allows for more targeted and efficient processing of domain-specific data, leading to improved performance and accuracy.
How does the dynamic activation and deactivation of parts in the model enhance its adaptability?
By activating or deactivating specific parts of the model based on the input data being processed, the model can adapt to different domains and optimize its performance for specific tasks.
Original Abstract Submitted
in various examples, systems and methods are disclosed that train a machine learning model(s)—such as a large language model (llm)—for one or more specific domains. in some embodiments, the machine learning model(s) may include at least a base model(s) as well as additional parts, such as additional layers, associated with the domains for which the machine learning model(s) is being trained. as such, the parts of the machine learning model(s) may be trained separately, such that training data associated with a domain is used to train a part of the machine learning model(s) that is associated with the domain without training the other part(s) of the machine learning model(s). the systems and methods may then use these parts when deploying the machine learning model(s), such as by activating and/or deactivating parts based on the input data being processed.
